chatgpt-deepseek-bridge

Let ChatGPT (or any MCP client) delegate coding tasks to a local Hermes-backed coding agent, with async job management so slow model runs never block the MCP frontend.

This project is not an official OpenAI product and is not endorsed by OpenAI. It uses the official Secure MCP Tunnel and tunnel-client as the transport, and Hermes Agent as the local agent runtime. It is tested with DeepSeek models served through OpenCode Go (opencode-go provider), but the model/provider are Hermes configuration values — other Hermes-supported providers work without code changes.

What it does

ChatGPT (MCP client)
   │  tools: deepseek_investigate / deepseek_execute / deepseek_continue
   │         deepseek_job_status / deepseek_job_result / bridge_status
   ▼
OpenAI Secure MCP Tunnel (tunnel-client daemon)
   ▼
mcp_bridge_server.py  (stdio MCP server)
   ▼
bridge_core.py        (job queue, dedup, single-task lock, git verification)
   ▼
hermes chat -q -Q     (local agent, one-shot invocation)
   ▼
DeepSeek / any Hermes-supported model

Features

• Async jobs: investigate / execute / continue return a job_id in ~1s; Hermes keeps working in the background (5–30 min is normal).

• Job persistence: job state and results are stored on disk (state/jobs/<job_id>.json) — surviving MCP reconnects, tunnel restarts, and bridge process restarts.

• deepseek_job_status / deepseek_job_result: local reads only, never invoke the model again, zero extra token cost.

• Idempotency: identical requests (tool + normalized task + project root) within a dedup window reuse the existing job — a retried execute never runs a second writer agent.

• Single-task lock with TTL + dead-process detection; safe queueing.

• Read-only verification: investigate is checked with git before/after snapshots; any change is reported as a violation.

• Safety recovery: on Hermes session-persistence failures the bridge retries automatically — investigate only when verified read-only; execute/continue only when no side effects were detected, otherwise it spawns a fresh session that completes only the remaining work (partial_execution_possible flag, original session id preserved).

• Watchdog: auto-restarts a dead tunnel daemon (~30s).

• Windows notifications: tray bubble on job completion/failure (fails silently, never affects job results).

• Environment hardening: spawns Hermes with validated TEMP/TMP/TMPDIR and verifies the Hermes home / session DB are writable.

Requirements

• Windows (PowerShell 7+; scripts use pwsh)

• Python 3.11+

• Hermes Agent CLI installed and configured with a model provider (e.g. DeepSeek via opencode-go)

• An OpenAI account with Developer Mode and a Secure MCP Tunnel (create at https://platform.openai.com/settings/organization/tunnels)

• A git repository you want the bridge to operate on

Installation

pwsh -NoProfile -ExecutionPolicy Bypass -File .\scripts\install-chatgpt-deepseek.ps1

This creates .venv, installs the pinned mcp==1.28.1 SDK, locates hermes.exe (config → -HermesPath → common install locations), and downloads the official tunnel-client release from GitHub. Add -SkipTunnelClient to skip the download (bridge-only setups).

Configuration

Copy the templates and fill in your values:

Copy-Item .env.example .env
Copy-Item config.example.json config.json

• .env → CONTROL_PLANE_API_KEY=... — your tunnel runtime key (create in the OpenAI tunnels page; this is not the tunnel id). Never commit this file.

• config.json →

  • project_root: absolute path of the git project the bridge may operate on

  • hermes_command: absolute path to hermes.exe

  • model / provider: any Hermes-supported combination (defaults are DeepSeek via opencode-go)

Running the tests

Requires Python 3.11+ with only requirements.txt installed (see Installation) and a local config.json (see Configuration) whose project_root points at any directory. The stdio compat test (tests/test_server_discover_compat.py) bootstraps a minimal config on a clean checkout; the bridge_core tests (test_recovery.py, test_async_flow.py, tests/test_git_verification.py) import bridge_core at load time and need config.json to already exist. test_async_flow.py spawns the configured hermes_command — point it at a real agent CLI, or a stub that prints an answer and exits 0.

\.venv\Scripts\python.exe tests	est_server_discover_compat.py
\.venv\Scripts\python.exe tests	est_git_verification.py
\.venv\Scripts\python.exe test_recovery.py
\.venv\Scripts\python.exe test_async_flow.py

Git must be available on PATH for verification; if it is not, jobs fail with error_type: git_verification_failed instead of silently reporting a verified run.

Starting the bridge

pwsh -NoProfile -File .\scripts\init-tunnel.ps1 -TunnelId tunnel_xxxxx
pwsh -NoProfile -File .\scripts\start-chatgpt-deepseek.ps1
pwsh -NoProfile -File .\scripts\status-chatgpt-deepseek.ps1
pwsh -NoProfile -File .\scripts\stop-chatgpt-deepseek.ps1

start launches tunnel-client (which spawns the bridge server) plus a watchdog that auto-restarts the daemon if it dies. Then, in ChatGPT → Developer Mode → Plugins, create a developer app and connect it to your tunnel id. Use Refresh Tools after any tool description change.

MCP tools

Async job workflow (for MCP clients)

1. Call deepseek_investigate / deepseek_execute / deepseek_continue → you get {status: "accepted", job_id, mode} almost immediately.

2. Do not poll in a tight loop. Jobs commonly take 5–30 minutes. Check deepseek_job_status at most once every 5 minutes; if you cannot defer a check that long, return control to the user and tell them the job is running in the background. A Windows notification appears when it finishes.

3. Once status is completed, call deepseek_job_result once and stop polling. It only reads saved data — no model quota is consumed.

4. deepseek_continue is for new work on an old session, not for fetching a previous job's result.

Security model / limitations

See SECURITY.md — read it before deploying.

• This bridge drives a local agent with shell and filesystem access.

• investigate read-only is enforced by git before/after verification, not by an OS sandbox.

• Only point the bridge at projects you trust (agent reads AGENTS.md etc.).

• The tunnel credential in .env is high-value — protect it, rotate it, never commit it.

Troubleshooting

• ChatGPT reports "connection timed out" on a tool call: the model run exceeded the MCP frontend's patience; the job keeps running in the background. Wait for the local notification, then call deepseek_job_result with the job_id. The watchdog restores a dead daemon within ~30s.

• warning: Failed to set cwd to temp dir in Hermes output: emitted by the uv trampoline when TEMP/TMP is invalid; non-fatal. The bridge now sanitizes these variables before spawning Hermes.

• hermes_failed with session persistence errors: the bridge auto-recovers (see Features). If jobs keep failing, run hermes sessions repair while no Hermes process is running.

• tunnel-client exits at startup: make sure CONTROL_PLANE_API_KEY in .env is the runtime key (starts with sk-proj-), not the tunnel id (starts with tunnel_).

• MSYS/git hangs: the bridge isolates git through a helper process; if you still see hangs, avoid running the bridge from an MSYS shell.

License

MIT — see LICENSE. The tunnel-client binary is distributed under Apache-2.0 by OpenAI (downloaded at install time, not bundled here).